INFO 2950

INFO 2950

Course information provided by the Courses of Study 2017-2018.

Teaches basic mathematical methods for information science, with applications to data science. Topics include discrete probability, Bayesian methods, graph theory, power law distributions, Markov models, and hidden Markov models. Uses examples and applications from various areas of information science such as the structure of the web, genomics, social networks, natural language processing, and signal processing.  Assignments require python programming.

When Offered Spring.

Prerequisites/Corequisites Prerequisite: A strong performance in an introductory statistics course from the approved list of accepted statistics courses found at and an introductory programming class with an ability to write and debug programs, or permission of instructor.

Distribution Category (MQR-AS)

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Enrollment Information
Syllabi: 1 available
  •   Regular Academic Session.  Choose one lecture and one discussion.

  • 4 Credits Stdnt Opt

  • 12148INFO 2950  LEC 001

  • Information Science majors must complete this class prior to their senior year. If you would like to enroll in this class, but are unable to, please contact the instructor via email at

  • 17426INFO 2950  DIS 201

  • 17893INFO 2950  DIS 202

  • 17894INFO 2950  DIS 203

  • 17895INFO 2950  DIS 204

  • 18246INFO 2950  DIS 205